This invention relates to an associative storage and an associative storing method, and in particular to those capable of storing new associative memories additionally and self-organizably without any interference with previously stored associative memories.
In human memories, usually several events, such as words, are memorized being associated with each other. For example, if a word "apple" is associated with a word "red" and then the word "red" is associated with the word "traffic light", association is initiated by one event, "apple", and progressed thereafter such that "apple is red" and "red is traffic light". When humans memorize some events, such events are usually memorized as being associated with other events and are also automatically associated with previously memorized events.
As described in "Computer Encyclopedia the Great" (Konpyuta dai-hyakka in Japanese), pp959-961, an associative storage, which is able to store several events associating themselves with each other and output a previously stored event in response to an inputted event that has also been stored as being associated thereto, is known as one of prior associative storage. Furthermore, as an improved version of such kind of associative storage, and as disclosed in Japanese Unexamined Patent Publication (referred to as JP-A hereinafter) Sho. 61-277694, a new technology for preventing outstanding noises by executing a feedback on an output from a correlative matrix through a transposed matrix, on the associative storage is also known. However, in those prior methods, it is difficult to update stored associative relationships because those events must have been stored by defining the associative relationship among them beforehand.
Another method for performing unique associative storage is referred to as a back propagation method and is described in "PARALLEL DISTRIBUTED PROCESSING", 1986, MIT PRESS. This is the method for letting the associative storage "learn", i.e. updating the stored associative relationships, by using a neural network. In this method, since the associative storage is able to be learned using the neural network, this system is not as rigid as the aforementioned method using the correlative matrix. However, there is a drawback that it requires much time to learn associative relationships among events to update the same. In addition, since this system updates associative relationships by utilizing prearranged groups of events to be stored, it is also required to relearn the entire prearranged groups in order to add a new group to be memorized.
JP-A Hei. 8-161894, although not prior art, discloses an associative storage device utilizing a method for learning associative relationships among events by using associative storage elements capable of outputting an output signal only when two input signals are simultaneously inputted thereto and capable of storing such states of storage elements. In this associative storage, since the states of storage elements in which two signals are inputted simultaneously thereto is utilized for the learning process, the system is not so rigid as the aforementioned method using the correlative matrix. In addition, since a respective associative storage element itself memorize such state, this association storage device provides much more flexibility on editing groups of events to be stored compared to the associative storage using the neural network. However, in this device, since the associative relationships among events are established at the time of learning of the state of respective storage elements, it is still difficult to enhance associated relationships among previously stored events.